Improving multiscale recurrent pattern image coding with enhanced dictionary updating strategies

The multidimensional multiscale parser (MMP) is a lossy multidimensional signal encoder, that uses an adaptive dictionary for approximating the original signal using multiscale recurrent pattern matching. In previous work we have shown the efficiency of MMP for image coding and we have also described new techniques to improve its performance, using predictive coding (MMP-Intra) and innovative strategies for reducing the dictionary redundancy. The combination of these methods for image coding achieves much better results than the state-of-the-art JPEG2000 and H.264/AVC Intra image encoders for text and compound images, but for smooth natural images it still presents small losses. In this work we present a new technique to improve the dictionary adaptation process of the MMP-Intra, based on enhanced updating techniques. Experimental results have showed that, when combined with dictionary growth control methods, this technique achieves consistent image quality gains for all image types. Furthermore we present some methods that eliminate the intrinsic substantial increase of the computational complexity associated with more rapidly growing dictionaries, without compromising the final quality of the decoded image.